This article applies to:
E-Prime 3.0
E-Prime 1.0
Detail
Experiment Author: Swets, John, Adapted from STEP and used with permission of Brian MacWhinney
Experiment Description
This is a classic experiment on signal detection, involving detecting a tone in white noise.
Experiment Instructions
Participants are presented instructions to listen for a tone in a burst of white noise. If there is a tone, participants press 'S' on a keyboard or 'N' if there is no tone. Headphones are required to complete the task. The experiment has one list with four samples. Two samples have no tone among the white noise, and two samples include a tone in the white noise. The samples are each presented 10 times each, with every trial randomly selecting from the list of four. The sound files each have a one second duration and participants are given five seconds to respond with an 'S' or an 'N'
Experiment Citation
Swets, John (1986) Indices of discrimination or diagnostic accuracy: Their ROCs and implied models. Psychological Bulletin, 99, 100-117.
Experiment Abstract or Original Experiment Abstract
Tasks in which an observation is the basis for discriminating between two confusable alternatives are used widely in psychological experiments. Similar tasks occur routinely in any practical settings in which the objective is a diagnosis of some kind. Several indices have been proposed to quantify the accuracy of discrimination, whether the focus is on an observer's capacity or skill, on the usefulness of tools designed to aid an observer, or on the capability of a fully automated device. The suggestion treated here is that candidate indices be evaluated by calculating their relative operating characteristics (ROCs). The form of an index's ROC identifies the model of the discrimination process that is implied by the index, and that theoretical form can be compared with the form of empirical ROCs. If an index and its model yield a grossly different form of ROC than is observed in the data, then the model is invalid and the index will be unreliable. Most existing indices imply invalid models. A few indices are suitable; one is recommended.
E-Prime 3.0
E-Prime 1.0
Detail
Experiment Author: Swets, John, Adapted from STEP and used with permission of Brian MacWhinney
Experiment Description
This is a classic experiment on signal detection, involving detecting a tone in white noise.
Experiment Instructions
Participants are presented instructions to listen for a tone in a burst of white noise. If there is a tone, participants press 'S' on a keyboard or 'N' if there is no tone. Headphones are required to complete the task. The experiment has one list with four samples. Two samples have no tone among the white noise, and two samples include a tone in the white noise. The samples are each presented 10 times each, with every trial randomly selecting from the list of four. The sound files each have a one second duration and participants are given five seconds to respond with an 'S' or an 'N'
Experiment Citation
Swets, John (1986) Indices of discrimination or diagnostic accuracy: Their ROCs and implied models. Psychological Bulletin, 99, 100-117.
Experiment Abstract or Original Experiment Abstract
Tasks in which an observation is the basis for discriminating between two confusable alternatives are used widely in psychological experiments. Similar tasks occur routinely in any practical settings in which the objective is a diagnosis of some kind. Several indices have been proposed to quantify the accuracy of discrimination, whether the focus is on an observer's capacity or skill, on the usefulness of tools designed to aid an observer, or on the capability of a fully automated device. The suggestion treated here is that candidate indices be evaluated by calculating their relative operating characteristics (ROCs). The form of an index's ROC identifies the model of the discrimination process that is implied by the index, and that theoretical form can be compared with the form of empirical ROCs. If an index and its model yield a grossly different form of ROC than is observed in the data, then the model is invalid and the index will be unreliable. Most existing indices imply invalid models. A few indices are suitable; one is recommended.
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